1. Field of the Invention
This invention relates to calibration of a water distribution model.
2. Background Information
In many civil engineering applications, and in particular, in water distribution networks for cities, townships and municipalities, it is of the utmost importance to have predictive simulations for the water pipe flow and hydraulic pressure conditions for the water network in that area. This is to ensure the availability of the potable water resource to the community, as well as to be able to perform vulnerability studies to produce risk assessments for risks that may be presented, which could affect the water supply.
A water distribution model is employed for these purposes in which data describing field conditions are assembled in a systematic manner to describe pipe flow and junction hydraulic grade lines (HGL), which are pressures, within the water network. The model is desirably capable of simulating conditions encountered at the site.
Engineers and modelers often calibrate the models they construct. A calibration involves the process of adjusting model characteristics and parameters so that the model predicted flows and pressures match actual observed field data to some desirable or acceptable level. Model calibration would not be necessary if the mathematical model exactly represented the actual physical processes and there was a perfect knowledge of all required parameters. Unfortunately, these criteria are not generally met; thus calibration is considered the most desirable way to achieve accurate model adjustment to most closely represent the water network being simulated.
Traditionally, methods for calibrating water distribution network models rely upon field measurement of network pressures, pipe flows, and water levels in storage facilities. The model is constructed, and then field observations are made by an engineer who visits various locations on the site and takes field observed measurements of pipe flows, water levels and pressures. The model is then adjusted on a trial and error basis so that the model simulation result more closely represents the observed data.
For example, a model representation of a water network may be developed which may include information about 12-inch mains, major 8-inch mains and loops and pipes that connect to sampling sites. A roughness coefficient is assumed for all pipes. Another aspect of the network is that of “demand.” “Demand,” as used herein, relates to the consumer demand for water at a given point in time. Demand patterns can be estimated based on the number of structures of different types in conjunction with an average water use by structures. Using this information, a working model is built to indicate how the network will behave in the real world to determine, for example, how much water is used at certain points in the network. Information is inserted into the working model, such as pipe roughness, and a basic working model is produced.
At this point, the model is then calibrated. As noted, prior techniques involved a trial and error process by which an engineer or modeler monitors various values such as pressure and flow to obtain a predicted model to compare to the observed data. If the predicted model does not compare closely with the observed data, the engineer returns to the working model, makes some adjustments, and runs it again to produce a new set of simulation results. This may have to be repeated many times to make sure that the working model produces a close enough prediction of water network behavior in the real world.
There are several disadvantages to the traditional calibration methods. One such disadvantage is, in a steady-state simulation, it is desired to provide no changes during field observation in the relevant aspects of the network. And yet, the observation itself could incorporate data from different network states. More specifically, an engineer or perhaps several engineers, take measurements in the field sequentially. During the time elapsed between taking the various measurements, the state or condition of certain aspects of the network can change. A simple, but illustrative, example is that of an engineer measuring pipe flow at location A, at which time a network pump may be in an “ON” position, thus the pump is operating. Later, when the engineer takes a field observed measurement at location B, the pump may now be in an “OFF” state, which would change pipe flow (and pressure) readings within the network. The network has changed during the observance of the data in the field, thus affecting the accuracy of the results.
A further disadvantage of traditional modeling techniques is that they are, among other things, quite time consuming. A typical network representation of a water network may include hundreds or thousands of links and nodes. Ideally, during a water distribution model calibration process, the roughness coefficient and pipe diameter is adjusted for each link, and demand adjusted for each node. Typically, however, only a percentage of representative sample measurements are used in a model, due in part to the time and labor requirements associated with gathering the evidence.
In addition, the model calibration process conventionally used does not take into account user weighted observation data such that the user can adjust hydraulic grade line (HGL) and/or pipe flow at data points of particular interest or importance to the user. Furthermore, known model calibration techniques use only one input parameter, pipe roughness, and this parameter is typically not weighted for the particular network involved.
In addition, when model calibration software is employed the software is run and a set of results is produced. However, the user cannot terminate or pause the application during run time to observe data at particular points, but instead, must wait until the application runs in full to then observe a single calibration solution so produced.
There remains a need, therefor, for a calibration process that results in a highly accurate model of a water distribution network. There remains a further need for such a process that does not involve undue amounts of trial and error in which multiple monitoring and measuring visits to the site must occur. There remains a further need for a method which produces a more reliable model, and allows the user to employ a number of weighted parameters which more accurately reflect the particular network being modeled in order to customize that model so that it more closely represents the actual behavior of the network.
There remains a further need for a modeling system in which the calibration can be performed automatically and which calibration process can be refined and manually adjusted during the calibration run time.
It is an object of the present invention to provide a calibration system that achieves these goals and that includes automatic calibration that takes into account a number of parameters and boundary conditions.